Implementation of surface crack detection method for nuclear fuel pellets by weakly supervised learning

Abstract

ABSTRACT Surface cracks are one of the primary defects in nuclear fuel pellets, posing a significant hazard to nuclear safety production. Deep learning-based methods recently developed for crack detection are typically trained using a supervised learning, with detection performance dependent on pixel-level annotations, which suffer from high labeling costs and low efficiency. To address this issue, we propose a Weakly Supervised Crack Detection (WSCD) network for surface crack detection of nuclear fuel pellets. The method adopts bounding-box annotations instead of pixel-level annotations, and rapidly generates pixel-level pseudo-labels using the designed Local Fusion Segmentation (LFS) module. Leveraging the Mask-RCNN network as the backbone, the network introduces spatial attention mechanisms to optimize feature extraction networks, enhancing the extraction capability of multi-scale crack features. Lastly, a novel loss function is optimized to address sample imbalance issues and expedite network convergence. Experimental results on the established crack dataset demonstrate that the proposed method improves labeling efficiency by approximately 20 times, achieving a segmentation accuracy (IoU) of 81.9%, which reaches 92.2% of the segmentation accuracy achieved by supervised learning and outperforms that the IoU (80.3%) of other supervised crack segmentation network for nuclear fuel pellets, and meeting the precision requirements of nuclear fuel pellets production lines.